Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks

TL;DR

This paper introduces Discriminative Adversarial Privacy (DAP), a new technique that balances deep learning model accuracy, training efficiency, and privacy against membership inference attacks, addressing the limitations of differential privacy.

Contribution

The paper proposes DAP, a novel adversarial training method with a new loss function and a metric called AOP to optimize the trade-off between privacy and performance.

Findings

DAP outperforms traditional differential privacy in accuracy and training time.

The AOP metric effectively captures the privacy-performance trade-off.

Experimental results validate DAP's ability to balance privacy and model utility.

Abstract

The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines. As the evolution of sophisticated Membership Inference Attacks (MIAs) threatens the secrecy of individual-specific information used for training deep learning models, Differential Privacy (DP) raises as one of the most utilized techniques to protect models against malicious attacks. However, despite its proven theoretical properties, DP can significantly hamper model performance and increase training time, turning its use impractical in real-world scenarios. Tackling this issue, we present Discriminative Adversarial Privacy (DAP), a novel learning technique designed to address the limitations of DP by achieving a balance between model performance, speed, and privacy. DAP relies on adversarial training based on a novel loss function able to minimise the prediction error while maximising the MIA's error. In addition, we introduce a novel metric named Accuracy Over Privacy (AOP) to capture the performance-privacy trade-off. Finally, to validate our claims, we compare DAP with diverse DP scenarios, providing an analysis of the results from performance, time, and privacy preservation perspectives.